The visibility of road images may be degraded when captured due to natural atmospheric phenomena such as haze, fog, sandstorms, and so forth. Such visibility degradation is due to the absorption and scattering of light by atmospheric particles. Road image degradation may cause problems for intelligent transportation systems (ITS) such as traveling vehicle data recorders and traffic surveillance systems, which must operate under variable weather conditions. The amount of absorption and scattering depends on the depth of the scene between a traffic camera and a scene point. Hence, scene depth information is important for recovering scene radiance in images captured in a hazy environment.
In order to improve visibility in hazy images, haze removal techniques have been recently proposed. These may be divided into two principal classifications: a given depth approach and an unknown depth approach.
The given depth approach relies on the assumption that the depth information is given, where the depth information is used for restoring hazy images. However, such approach is not suitable for haze removal in real-world applications because the depth information needs to be provided by the user. Therefore, many studies have proposed the estimation of an unknown depth to recover scene radiance in hazy images.
The unknown depth approach may be divided into two major categories: a multiple-image restoration technique and a single-image restoration technique. The multiple-image restoration technique mainly requires either a complex computation or a use of additional hardware devices. This may lead to costly restoration expenses. Hence, recent research has been focusing on the single-image restoration technique for estimating the unknown depth without any additional information to recover scene radiance in hazy images.
A prior art method proposes a single-image haze removal approach that removes haze by maximizing the local contrast of recovered scene radiance based on an observation that captured hazy images have lower contrast than restored images. However, such approach may result in unwanted feature artifact effects along depth edges. Another prior art method proposes another haze removal technique for single images that estimates the albedo of the scene and deduces the transmission map based on an assumption that the transmission shading and the surface shading are locally uncorrelated. However, such method may not contend with images featuring dense fog. Also, another prior art method describes a characteristic property in which smaller transmission intensity values possess large coefficients in a gradient domain, whereas larger transmission intensity values possess smaller coefficients. Based on the property, the visibility of hazy images may be restored by employing a multi-scale technique in the regions containing small transmission values. However, such method may result in excessive restoration with regard to the sky regions of a resultant image.
Yet another prior art proposes a haze removal algorithm via a dark channel prior technique based on an observation that at least one color channel is composed of pixels having lower intensities within local patches in outdoor haze-free images to effectively remove haze formation while only using a single image. Until now, such approach has attracted the most attention due to its ability to effectively remove haze formation while only using a single image. Inspired by the dark channel prior technique, an improved haze removal algorithm is proposed by employing a scheme consisting of a dark channel prior and a multi-scale Retinex technique for quickly restoring hazy images.
Nevertheless, the scene radiance recovered via the aforesaid dark channel prior based techniques may be accompanied by the generation of serious artifacts when a captured hazy road image contains localized light sources or color-shift problems due to sandstorm conditions. This may be problematic for many common road scenarios. For example, in inclement weather conditions, drivers may turn on headlights of vehicles and streetlights may be activated in order to improve visual perception. The aforesaid dark channel prior based techniques may fail to produce satisfactory restoration results when presented with these scenarios.